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DBMS > Databend vs. Google Cloud Datastore vs. Microsoft Azure Synapse Analytics vs. Milvus

System Properties Comparison Databend vs. Google Cloud Datastore vs. Microsoft Azure Synapse Analytics vs. Milvus

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonMilvus  Xexclude from comparison
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformElastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerA DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelRelational DBMSDocument storeRelational DBMSVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.30
Rank#287  Overall
#130  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score20.56
Rank#31  Overall
#19  Relational DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
cloud.google.com/­datastoreazure.microsoft.com/­services/­synapse-analyticsmilvus.io
Technical documentationdocs.databend.comcloud.google.com/­datastore/­docsdocs.microsoft.com/­azure/­synapse-analyticsmilvus.io/­docs/­overview.md
DeveloperDatabend LabsGoogleMicrosoft
Initial release2021200820162019
Current release1.0.59, April 20232.3.4, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Zilliz Cloud – Cloud-native service for Milvus
Implementation languageRustC++C++, Go
Server operating systemshosted
Linux
macOS
hostedhostedLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyes, details hereyesVector, Numeric and String
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nononono
Secondary indexesnoyesyesno
SQL infoSupport of SQLyesSQL-like query language (GQL)yesno
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
RESTful HTTP API
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
Java
PHP
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnousing Google App EngineTransact SQLno
TriggersnoCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding, horizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication using Paxosyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsno infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yesRole based access control and fine grained access rights
More information provided by the system vendor
DatabendGoogle Cloud DatastoreMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseMilvus
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosRAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Key customersMilvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricsAs of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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DatabendGoogle Cloud DatastoreMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseMilvus
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